Small-scale fisheries are Too Big To Ignore

Too Big to Ignore is a new research network and knowledge mobilization partnership to promote and revitalize small-scale fisheries.

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New Book: Governability of Fisheries and Aquaculture

This volume illustrates the contribution of interactive governance theory to understanding core fisheries and aquaculture challenges.

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Mission

Too Big To Ignore is a research network and knowledge mobilization partnership established to elevate the profile of small-scale fisheries (SSF), to argue against their marginalization in national and international policies, and to develop research and governance capacity to address global fisheries challenges

Facts & Figures

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State of the Art


Total annual catch

Total annual catch

Source: World Bank/FAO/WorldFish (2010)

Member of the Week

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Andrew Song

Andrew Song is an early career social scientist interested in the geography and governance of natural resources with particular reference to small-scale fisheries. He is currently a postdoctoral fellow at the Department of Natural Resource Sciences at McGill University, Canada. [read more]

News & Updates

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Sign up to TBTI new Research Clusters

TBTI is moving into a new phase with the organization of activities around twelve ‘research clusters’, several of which are linked to the original themes, but many are added ba ...[read more]

Information System for Small-scale Fisheries (ISSF) launched

TBTI has recently launched its Information System on Small-scale Fisheries (ISSF), the first interactive global repository of small-scale fisheries, providing information on key ...[read more]

WHO WE ARE

Officially launched in September 2012, this six-year long Social Sciences and Humanities Research Council (SSHRC) project is an open network that encourages anyone interested in SS ...[read more]

Improved data needed on small-scale fisheries

Daniel Pauly and Tony Charles recently wrote a letter in Science about small-scale fisheries data needs. In it, they urge for an improved reporting of catch data that distinguish ...[read more]